Navegando por Assunto "Reconhecimento automático da voz"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Avanços em reconhecimento de fala para português brasileiro e aplicações: ditado no libreoffice e unidade de resposta audível com asterisk
(Universidade Federal do Pará, 2013-03-04) BATISTA, Pedro dos Santos; SAMPAIO NETO, Nelson Cruz; http://lattes.cnpq.br/9756167788721062; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
Automatic speech recognition has been increasingly more useful and feasible. When it comes to languages such as English, there are excellent speech recognizers available. However, the situation is not the same for Brazilian Portuguese, where the few recognizers for desktop dictation that existed, are no longer available. This dissertation is aligned with a goal of the Signal Processing Laboratory at the Federal University of Para, which is the development of a complete automatic speech recognizer for Brazilian Portuguese. More specifically, the main contributions of this dissertation are: the development of some resources needed to build a speech recognizer such as transcribed audio database and speech API; and the development of two applications: one for desktop dictation and another for automatic service in a call center. The system developed in-house for automatic speech recognition in Brazilian Portuguese is called Coruja, and besides all the resources that makes automatic speech recognition in Brazilian Portuguese available, the Coruja also contains an API for application development using speech recognition. The application for desktop dictation is called SpeechOO. The SpeechOO enables dictation and text editing and formatting by voice for the LibreOffice Writer. Other contribution of this work is the use of Coruja in call centers. Coruja was integrated with the Asterisk software, which is the main open source software for call centers. The main application developed for automated service in call center was an interactive voice response which is deployed nationally and receives more than 3 thousand daily calls.
Acesso aberto (Open Access)
Conversão grafema-fone para um sistema de reconhecimento de voz com suporte a grandes vocabulários para o português brasileiro
(Universidade Federal do Pará, 2006-06-12) HOSN, Chadia Nadim Aboul; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
Speech processing has become a data-driven technology. Hence, the success of research in this area is linked to the existence of public corpora and associated resources, as a phonetic dictionary. In contrast to other languages such as English, one cannot find, in public domain, a Large Vocabulary Continuos Speech Recognition (LVCSR) System for Brazilian Portuguese. This work discusses some efforts within the FalaBrasil initiative [1], developed by researchers, teachers and students of the Signal Processing Laboratory (LaPS) at UFPA, providing an overview of the research and softwares related to Automatic Speech Recognition (ASR) for Brazilian Portuguese. More specifically, the present work discusses the implementation of a large vocabulary ASR for Brazilian Portuguese using the HTK software, which is based on hidden Markov models (HMM). Besides, the work discusses the implementation of a grapheme-phoneme conversion module using machine learning techniques.
Acesso aberto (Open Access)
Ferramentas e recursos livres para reconhecimento e síntese de voz em português brasileiro
(Universidade Federal do Pará, 2011-06-17) SAMPAIO NETO, Nelson Cruz; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
Automatic speech recognition and text-to-speech systems have modules that depend on the language and, while there are many public resources for some languages (e.g. English and Japanese), the resources for Brazilian Portuguese (BP) are still limited. Another aspect is that for many tasks the current speech recognition system error rate is still high, when compared to that obtained by humans. Thus, despite the success of hidden Markov models (HMM), it is necessary to investigate new methods. This work has these two facts as motivation and is divided into two parts. The first part describes the resources and free tools developed for BP speech recognition and synthesis, consisting of text and audio databases, phonetic dictionary, grapheme-to-phone converter, syllabification module, language and acoustic models. All of them are publicly available and, together with a proposed application programming interface, have been used for the development of several new real-time applications, including a speech module for the OpenOffice suite. Performance tests are presented for evaluating the developed systems. The resources make easier the adoption of BP speech technologies by other academic groups, developers and industry. The second part of this work presents a new method for rescoring the recognition result obtained via HMMs, with the result being organized as a lattice. More specifically, the system uses discriminative classifiers that aim at reducing the confusability between pairs of phones. For each of these binary problems, automatic feature selection techniques are used to choose the proper parametric representation for the specific problem.